Kb Github Analytics Scoring logo

Kb Github Analytics Scoring

Organization
Community-Access
kb-github-analytics-scoring

Reference data, not a reviewer. Score repository health (0-100, A-F grades), priorities, and delta tracking. Includes velocity metrics, confidence levels, and bottleneck detection.

Overview

PublisherCommunity-Access
Repositoryaccessibility-agents
Skill namekb-github-analytics-scoring
Stars
429
Forks
51
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Community-Access on GitHub. Read the source before you install it.

Installation

Install the Kb Github Analytics Scoring AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Community-Access/accessibility-agents/tree/main/skills/kb-github-analytics-scoring
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Community-Access/accessibility-agents.git /tmp/accessibility-agents
mkdir -p .claude/skills
cp -r /tmp/accessibility-agents/skills/kb-github-analytics-scoring .claude/skills/kb-github-analytics-scoring
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kb Github Analytics Scoring in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kb Github Analytics Scoring on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kb Github Analytics Scoring is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

GitHub Analytics Scoring

Repository Health Score

text
Health Score = 100 - (sum of weighted penalties)

Penalties - CI & Reliability:
  Failing workflow, high confidence:         -10 points
  Flaky test (3+ failures/week):             - 5 points
  No CI configured:                          - 8 points

Penalties - Issues & PRs:
  Critical unresolved bug (P0):             -10 points
  Security alert (critical severity):       -10 points
  Security alert (high severity):           - 5 points
  PR open >14 days (stale):                 - 3 points each (max -9)
  Issue @mentioned, no response >7 days:    - 5 points each (max -10)
  >10 open issues with no labels/triage:    - 3 points

Penalties - Community Health:
  No CONTRIBUTING.md:                        - 5 points
  No CODE_OF_CONDUCT.md:                     - 3 points
  No branch protection on main:              - 5 points
  0 open "good first issue" labels:          - 2 points

Floor: 0 (minimum score)

Score Grades

Each score, with its grade and meaning.

ScoreGradeMeaning
90-100AExcellent - healthy repo, minimal issues
75-89BGood - minor issues, well-managed
50-74CNeeds attention - multiple signals requiring action
25-49DPoor - significant problems impacting velocity
0-24FCritical - major issues blocking team progress

Priority Scoring for Issues

Internally score each issue to determine sort order in dashboards and reports:

SignalPoints
User @mentioned and hasn't responded+5
P0, P1, critical, urgent, blocker label+3
Tied to upcoming release milestone+3
New comments from others since user's last activity+2
High community interest (5+ positive reactions)+2
bug label+1
Assigned to user+1
Active related discussion thread+1
wontfix, duplicate, question label-1
No activity >14 days-2

Priority Scoring for Pull Requests

Each signal, with its points.

SignalPoints
Review requested, user hasn't reviewed+5
PR has "changes requested" - author needs to update+4
PR is approved and ready to merge+3
Targets a release branch with upcoming deadline+3
CI failed on this PR+2
Merge conflicts present+2
High community interest (5+ positive reactions)+2
New comments on the PR+1
Active discussion thread linked to this PR+1
Draft PR-1
No activity >7 days-2

Priority Scoring for Discussions

Each signal, with its points.

SignalPoints
User was @mentioned+3
High activity (10+ comments in 24h)+2
Linked to an issue/PR the user owns+1
No activity >7 days-1

Confidence Levels for Analytics Findings

Apply confidence levels to all bottleneck findings, health score deductions, and anomaly detections. Every finding in a report MUST include its confidence level.

LevelWhen to UseImpact on Report
HighMultiple corroborating signals, definitively observed (e.g., CI has failed 5 times this week, confirmed by logs)Report as fact; recommend remediation
MediumFound by one source, likely issue (e.g., PR open 14 days but author may be on leave)Report as finding; suggest investigation
LowPossible pattern, needs human review (e.g., velocity seems low but may be seasonal)Flag as observation; don't deduct from score

Confidence Tag Format in Reports

markdown
| Finding | Severity | Confidence | Recommendation |
|---------|----------|-----------|----------------|
| 3 PRs stale >14 days | High | **High** | Assign reviewers or close |
| Velocity 40% below 4-week avg | Medium | **Medium** | Verify - may be holiday period |
| Issue activity spike on Fridays | Low | **Low** | Monitor for 2 weeks before acting |

Delta Tracking

When re-running an analysis against a previous report, classify every finding by change status:

StatusDefinition
FixedWas in previous report; no longer present
NewNot in previous report; appears now
PersistentRemains from previous report, unchanged
RegressedWas previously fixed but has returned

Delta Report Section Format

markdown
## Changes Since Last Report ({previous date})

| Change | Finding | Details |
|--------|---------|---------|
|  Fixed | 3 stale PRs resolved | Team cleared the backlog |
|  New | CI failing on main | Build broke in last 24h |
|  Persistent (#3) | Velocity 30% below baseline | Third consecutive week |
|  Regressed | Security alert reopened | lodash advisory re-filed |

**Progress:** {N} fixed, {M} new, {P} persistent, {Q} regressed
**Score change:** {current_score} ({+/-N} from {previous_score})

Escalation Rule

If a finding is Persistent for 3+ consecutive reports, escalate:

Escalation: [{Finding}] has been present for {N} consecutive reports. Consider a team discussion or explicit ownership assignment to resolve this.


Velocity Metrics

Each metric, with its formula, healthy, warning and critical.

MetricFormulaHealthyWarningCritical
PR merge ratePRs merged / week>3/week1-3/week<1/week
Issue close rateIssues closed / week>5/week2-5/week<2/week
Review turnaroundAvg hours PR open before first review<24h24-72h>72h
Issue response timeAvg hours until first team response<8h8-48h>48h
Stale PR ratioPRs open >14 days / total open PRs<10%10-30%>30%
CI reliabilityPassing runs / total runs (7-day)>95%80-95%<80%

Contributor Metrics

Each metric, with its formula and notes.

MetricFormulaNotes
PR commit ratioCommits / PRs mergedHigh ratio = large PRs, consider splitting
Review coveragePRs reviewed by others / total PRs mergedLow = single-contributor bottleneck
First-time contributor rateNew contributors / total active contributorsHealthy community growth signal
Contributor retentionActive last month AND active this month / active last monthEngagement health
Bus factorMin contributors whose departure halts 50%+ of commits<3 is high risk

Bottleneck Categories

Each category, with its signals and example finding.

CategorySignalsExample Finding
Review bottleneckHigh PR open time, review-requested PRs >3 days"4 PRs waiting for review >3 days - assign or redistribute"
CI bottleneckFrequent failures, long runtimes"Test suite 45min avg - 2x slower than 4-week average"
Stale workIssues/PRs with no activity >14 days"7 issues open >30 days with no recent comments"
Knowledge concentration80%+ commits from one contributor"3 of 4 core modules have only one contributor - bus factor 1"
Response lag>24h average first response to new issues"Average first response: 38h - community may feel unsupported"
Security debtOpen Dependabot alerts unresolved >7 days"2 critical alerts open for 12 days"
Release congestion>10 merged PRs unreleased"11 merged PRs awaiting release - coordinate cut date"

Trend Classification

Each trend, with its definition.

TrendDefinition
ImprovingScore increased 5+ points vs previous report
StableScore within 5 points of previous report
DecliningScore decreased 5+ points vs previous report
RecoveringScore increased after 2+ consecutive Declining reports
NewNo previous report to compare

Scorecard Output Format

Single Repo

markdown
## Repository Health Score

| Metric | Value |
|--------|-------|
| Repo | [owner/repo](url) |
| Score | {0-100} |
| Grade | {A-F} |
| Trend | {Improving / Stable / Declining} |
| Open Security Alerts | {count} |
| Stale PRs (>14 days) | {count} |
| CI Reliability (7-day) | {%} |
| Unanswered @mentions | {count} |

Multi-Repo Scorecard

markdown
## Repository Health Scorecard

| Repo | Score | Grade | Trend | Critical Issues | Notes |
|------|-------|-------|-------|-----------------|-------|
| owner/repo-a | 88 | B | Improving | 0 | |
| owner/repo-b | 54 | C | Declining | 1 | Security alert unresolved |
| owner/repo-c | 22 | F | Stable | 3 | Review bottleneck + stale PRs |
| **Average** | **54.7** | **C** | - | **4** | |

Sprint & Iteration Metrics

When project boards are configured, track sprint metrics:

MetricFormula
Sprint completion rateDone items / total sprint items x 100
Carryover rateItems moved to next sprint / planned items
Cycle timeAverage days from "In Progress" to "Done"
ThroughputItems completed per sprint
Blocked rateItems in "Blocked" state / total in-progress

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Kb Github Analytics Scoring AI skill do?

Reference data, not a reviewer. Score repository health (0-100, A-F grades), priorities, and delta tracking. Includes velocity metrics, confidence levels, and bottleneck detection.

Why use Kb Github Analytics Scoring on TypingMind?

Because you install it once and use it with any model. Kb Github Analytics Scoring is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kb Github Analytics Scoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Community-Access/accessibility-agents/tree/main/skills/kb-github-analytics-scoring. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Kb Github Analytics Scoring?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kb Github Analytics Scoring?

As many as you like. As long as a model supports skills, you can use Kb Github Analytics Scoring with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kb Github Analytics Scoring AI skill free?

Yes. It is published on GitHub by Community-Access under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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